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Top Document: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware Previous Document: How to learn an inverse of a function? Next Document: How to recognize handwritten characters? See reader questions & answers on this topic! - Help others by sharing your knowledge translation, rotation, etc.? ============================ See: Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford: Oxford University Press, section 8.7. Masters, T. (1994), Signal and Image Processing with Neural Networks: A C++ Sourcebook, NY: Wiley. Soucek, B., and The IRIS Group (1992), Fast Learning and Invariant Object Recognition, NY: Wiley. Squire, D. (1997), Model-Based Neural Networks for Invariant Pattern Recognition, http://cuiwww.unige.ch/~squire/publications.html Laurenz Wiskott, bibliography on "Unsupervised Learning of Invariances in Neural Systems" http://www.cnl.salk.edu/~wiskott/Bibliographies/LearningInvariances.html User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neural-nets FAQ, Part 7 of 7: Hardware Previous Document: How to learn an inverse of a function? Next Document: How to recognize handwritten characters? Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Part7 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
Last Update March 27 2014 @ 02:11 PM
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PDP++ is a neural-network simulation system written in C++, developed as an advanced version of the original PDP software from McClelland and Rumelhart's "Explorations in Parallel Distributed Processing Handbook" (1987). The software is designed for both novice users and researchers, providing flexibility and power in cognitive neuroscience studies. Featured in Randall C. O'Reilly and Yuko Munakata's "Computational Explorations in Cognitive Neuroscience" (2000), PDP++ supports a wide range of algorithms. These include feedforward and recurrent error backpropagation, with continuous and real-time models such as Almeida-Pineda. It also incorporates constraint satisfaction algorithms like Boltzmann Machines, Hopfield networks, and mean-field networks, as well as self-organizing learning algorithms, including Self-organizing Maps (SOM) and Hebbian learning. Additionally, it supports mixtures-of-experts models and the Leabra algorithm, which combines error-driven and Hebbian learning with k-Winners-Take-All inhibitory competition. PDP++ is a comprehensive tool for exploring neural network models in cognitive neuroscience.